World Map with Confirmed and Death Cases (Data source (JHU 2020b), see also (JHU 2020a))
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Bar Charts with descending order
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Case numbers are taken from (JHU 2020b). In order to compensate for the daily fluctuations, the mean case numbers for the past seven days (*_mp7d) were also added.
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Bar Charts with descending order
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Population numbers are taken from (CIA 2020a), (CIA 2020b). In order to compensate for the daily fluctuations, the mean case numbers for the past seven days (*_mp7d) were also added.
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Bar Chart with descending order
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The number of confirmed cases is an early predictor of the number of deaths. The number of today’s deaths is already determined by the infections about by \(\sim19\) days ago or respectively by the confirmed cases about by \(\sim11\) days ago. The Confirmed infection to Death period can be assumed to be an average of \(11\) days (see RWI 2020).
However, this varies considerably depending on country-specific test rate and health system. In the worst health systems it is only one day for recognized cases, the “Confirmed” cases must be “lagged” by \(\sim1\) day. In the best case, the time from the end of incubation period (in average \(\sim5-6\) days) to death is an average \(\sim19\) days. In this case, the average Confirmed infection to Death period is \(\sim14\) days), the “Confirmed” cases must be correctly “lagged” by \(\sim14\) days. For the assumed time periods see (RKI 2020c), (RKI 2020b), for Case Fatality Rate and Incubation Period in general see (Wikipedia contributors 2020a), (Wikipedia contributors 2020c).
The simple calculation with unlagged cumulated confirmed cases divided by cumulated deaths results in a significant underestimation of the CFR in health systems with early disease detection.
The Infection Fatality Rate (IFR) is the fatality rate of all infection, that means detected confirmed cases and undetected cases (asymptomatic and not tested group). This lethality is assumed to be country independent and only rough estimates exist (RKI: bottom of existing estimates \(\sim0.56\%\)).
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| Country | CFR_total | CFR_past_period | CFR_unlagged |
|---|---|---|---|
| Austria | 4.1 | 5.8 | 4.0 |
| France | 15.9 | 17.3 | 15.3 |
| Germany | 4.8 | 5.3 | 4.7 |
| India | 4.6 | 4.0 | 2.8 |
| Italy | 14.7 | 12.7 | 14.4 |
| South Korea | 2.4 | 1.2 | 2.3 |
| Spain | 11.5 | 4.5 | 11.2 |
| United States of America | 6.5 | 4.4 | 5.7 |
Cumulated and Daily Cases over Time
Selected Countries
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Germany - Rolling Mean and Reproduction Number
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The 7-day Rolling Mean/Moving Average of the Daily Confirmed and Death Cases smooths out the short-term weekly fluctuations (weekend).
The daily confirmed cases are related to the left y-axes, the daily death cases are related to the right y-axes. This clearly outlines the 12 days delay relation between daily confirmed and death cases and also the roughly the factor of ~1/25 (~4%).
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The calculation of the reproduction number \(R(t)\) uses a R function provided by (Thomas Hotz 2020b) on GitHub.
The (effective) reproduction number \(R(t)\) at day \(t\), i.e. the average number of people someone infected at time \(t\) would infect if conditions remained the same.
For further German federal states figures (based on the data provided by Robert Koch Institut) see (Thomas Hotz 2020a) and for worldwide figures (based on the data provided by Johns Hopkins University) see (Thomas Hotz 2020c).
For the calculation the assumption of 7-days reporting delay (confirmed is reported 7-days after ‘real’ infection) is unchanged and the same modelled infectivity profile w is used. The lower and upper confidence interval lines provide the (approximate, pointwise) 95% confidence interval (only based on statistical numbers, possible changes in e.g. counting measures can not be considered).
This is the reason why (Thomas Hotz 2020b) “do not compute an average over a sliding window of seven days so the viewer immediately recognizes the size of such artefacts, warning her to be overly confident in the results. In fact, these artefacts are much larger than the statistical uncertainty due to the stochastic nature of the epidemic which is reflected in the confidence intervals.”
Nevertheless, here the calculation is based on the 7-days rolling mean and therefore the figure smooths over the the weekly rhythm.
China and South Korea have slowed down significantly the exponential growth. Therefore, their lines in the chart with the log10 scale have no longer a significant slope.
In early phases countries have a more or less unchecked exponential growth. If countermeasures are effective, reduced exponential growth is reflected in a reduced slope of the accumulated cases again.
The plot shows the extreme forecast increase in case of unchecked exponentiell growth. The dark shaded regions show 80% rsp. 95% prediction intervals. These prediction intervals are displaying the uncertainty in forecasts based on the linear regression over the past 7 days.
The charts compare the different forecasts for an exponential rsp. linear growth model. Due to the large fluctuations of the daily cases regression of three weeks is required. Otherwise the prediction levels are much too big.
The dark shaded regions are indicating the \(80\%\) rsp. \(95\%\) prediction intervals. These prediction intervals are displaying the “pure” statistical uncertainty in forecasts based on the regression models.
For doubling periods in the order of period of infectivity (RKI assumption: \(\sim9-10\) days, with great uncertainty, see (RKI 2020b), we no longer have exponential growth. The “old” infected cases are at the end of the doubling period no longer infectious (active). This results in a constant infection rate with basic reproduction number \(R_0 \sim 1\) or even \(<1\).
Note: for case numbers of German federal states see (RKI 2020a).
Doubling Time and Forecast The forecasted cases for the next 14 days are calculated ‘only’ from the linear regression of the logarithmic data and are not considering any effects of measures in place. In addition data inaccuracies are not taken into account, especially relevant for the confirmed cases.
Therefore the 14 days forecast is only an indication for the direction of an unchecked exponentiell growth.
| Country | Case_Type | T_doubling | last_day | FC_next_day | FC_14days |
|---|---|---|---|---|---|
| Austria | Confirmed | 380.9 | 16’902 | 16’939 | 17’344 |
| France | Confirmed | 476.0 | 191’102 | 191’356 | 195’013 |
| Germany | Confirmed | 344.2 | 185’750 | 186’090 | 191’027 |
| India | Confirmed | 16.0 | 257’486 | 269’133 | 473’421 |
| Italy | Confirmed | 524.1 | 234’998 | 235’368 | 239’450 |
| South Korea | Confirmed | 176.9 | 11’814 | 11’861 | 12’480 |
| Spain | Confirmed | 510.7 | 241’550 | 241’937 | 246’244 |
| United States of America | Confirmed | 59.1 | 1’942’363 | 1’964’849 | 2’288’696 |
| World | Confirmed | 36.6 | 7’009’065 | 7’152’254 | 9’145’971 |
| Austria | Deaths | 650.4 | 672 | 673 | 683 |
| France | Deaths | 385.7 | 29’158 | 29’250 | 29’942 |
| Germany | Deaths | 251.2 | 8’685 | 8’720 | 9’038 |
| India | Deaths | 16.3 | 7’207 | 7’541 | 13’112 |
| Italy | Deaths | 314.8 | 33’899 | 33’985 | 34’972 |
| South Korea | Deaths | 1’762.9 | 273 | 273 | 275 |
| Spain | Deaths | 10’746.3 | 27’136 | 27’138 | 27’161 |
| United States of America | Deaths | 82.8 | 110’514 | 111’687 | 124’532 |
| World | Deaths | 57.9 | 402’730 | 409’178 | 478’026 |
The forecast accuracy is checked by using the forecast method for the nine days before the past three days (training data). Subsequent forecasting of the past three days enables comparison with the real data of these days (test data).
The comparison is also an early indicator if the exponential growth is declining. However, possible changes in underreporting (in particular the proportion confirmed / actually infected) requires careful interpretation.
For doubling periods in the order of infectivity (RKI assumption: \(\sim9-10\) days, with great uncertainty, (see RKI 2020b), we no longer have exponential growth. Since the “old” infected cases are no longer infectious after these periods and we then have a constant infection rate with basic reproduction number \(R_0 \sim 1\).
Instead, we have “only” linear growth of the cumulative Confimred Cases and the Daily Confirmed Cases remain more or less constant or even decrease.
However, the basic reproduction number \(R_0\) is a product of the average number of contacts of an infectious person per day, the probability of transmission upon contacts and the average number of days infected people are infectious. With the current uncertainty of the average duration of the infectivity duration, \(R_0\) can therefore be estimated from the doubling time only to a very limited extent. See also (CMMID 2020) and (RKI 2020d).
The number of confirmed cases is an early predictor of the number of deaths. The number of today’s deaths is already determined by the infections about by \(\sim19\) days ago or respectively by the confirmed cases about by \(\sim11\) days ago see Bar Chart - CFR.
The country specific case fatality rate (CFR, proportion of deaths from confirmed cases) indicates the country specific testing rate and may depend on quality/capacity of hospitals.
Overall a rough conclusion on the country specific underreporting rate (lack of diagnostic confirmation; proportion of all infected to confirmed cases) is feasible if the infection fatality rate (IFR, confiremd cases plus all asymptomatic and undiagnosed infections) is assumed to be country independent and the IFR is known (bottom of existing estimates \(\sim0.56\%\), assumption by RKI see (RKI 2020b).
In this case an estimation of the CFR of \(0.06\) \((6\%)\) indicates an underreporting by a by a factor of \(\sim10\). A CFR of \(0.20\) \((20\%)\) indicates an underreporting by a by a factor of \(\sim30\). This corresponds to RKI assumption of a underreporting by a factor of \(11-20\) (RKI 2020c). Unfortunately, the IFR or lethality is still far too imprecise for concrete conlusions.
In the model paper RKI assumes for the
Depending on the country-specific test frequency (late or early tests), the
*lag_days - time from receipt of the confirmed test result to death, Confirmed to Death, is about \(11-13\) days.
Note: these methods are also used for example for advertising campaigns. The campaign impact on sales will be some time beyond the end of the campaign, and sales in one month will depend on the advertising expenditure in each of the past few months (see Hyndman and Athanasopoulos 2020).
| Country | CFR_total | CFR_past_period | CFR_unlagged |
|---|---|---|---|
| Germany | 4.8 | 5.3 | 4.7 |
| Italy | 14.7 | 12.7 | 14.4 |
| Spain | 11.5 | 4.5 | 11.2 |
Forecast residuals indicate quality of fit with Arima model:
Data Source
Data files are provided by Johns Hopkins University on GitHub
https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series
The data are visualized on their excellent Dashboard
Johns Hopkins University Dashboard
https://coronavirus.jhu.edu/map.html
Code Source
Directory with all R Sources is replicated in GitHub repository:
https://github.com/WoVollmer/R-TimesSeriesAnalysis/tree/master/Corona-Virus
Code is based on ideas from https://rpubs.com/TimoBoll/583802
CIA. 2020a. “The World Factbook - Country Comparison :: Population.” Internet. https://www.cia.gov/library/publications/resources/the-world-factbook/fields/335rank.html.
———. 2020b. “The World Factbook - Population Raw Data.” Internet. https://www.cia.gov/library/publications/resources/the-world-factbook/fields/rawdata_335.txt.
CMMID. 2020. “Temporal Variation in Transmission During the Covid-19 Outbreak.” GitHub. https://cmmid.github.io/topics/covid19/current-patterns-transmission/global-time-varying-transmission.
Hyndman, Rob J, and George Athanasopoulos. 2020. “Forecasting: Principles and Practice.” Online textbook. https://otexts.com/fpp3/lagged-predictors.html.
JHU. 2020a. “Coronavirus - Dashboard.” Internet. https://coronavirus.jhu.edu/map.html.
———. 2020b. “Coronavirus - Time Series Data.” GitHub. https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series.
RKI. 2020a. “Coronavirus - Fallzahlen.” Germany: Internet. https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Fallzahlen.html?nn=13490888.
———. 2020b. “Coronavirus - Modellierung.” Germany: Internet. https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Modellierung_Deutschland.pdf?__blob=publicationFile.
———. 2020c. “Coronavirus - Steckbrief.” Germany: Internet. https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Steckbrief.html.
———. 2020d. “Schätzung Der Aktuellen Entwicklung Der Sars-Cov-2-Epidemie in Deutschland - Nowcasting.” Germany: Internet. https://www.rki.de/DE/Content/Infekt/EpidBull/Archiv/2020/17/Art_02.html?nn=13490888.
RWI. 2020. “Corona-Pandemie: Statistische Konzepte Und Ihre Grenzen.” Germany: Internet. http://www.rwi-essen.de/unstatistik/101/.
Thomas Hotz, Stefan Heyder, Matthias Glock. 2020a. “Monitoring Der Ausbreitung von Covid-19 Durch Schätzen Der Reproduktionszahl Im Verlauf Der Zeit Konzepte Und Ihre Grenzen; Deutschland.” Germany: Internet. https://stochastik-tu-ilmenau.github.io/COVID-19/germany.
———. 2020b. “Monitoring the Spread of Covid-19 by Estimating Reproduction Numbers over Time; Technical Report.” Germany: Internet. https://stochastik-tu-ilmenau.github.io/COVID-19/reports/repronum/repronum.pdf.
———. 2020c. “Monitoring the Spread of Covid-19 by Estimating Reproduction Numbers over Time; World & Selected Countries.” Germany: Internet. https://stochastik-tu-ilmenau.github.io/COVID-19/index.html.
Wikipedia contributors. 2020a. “Case Fatality Rate — Wikipedia, the Free Encyclopedia.” https://en.wikipedia.org/w/index.php?title=Case_fatality_rate.
———. 2020b. “Exponential Growth — Wikipedia, the Free Encyclopedia.” https://en.wikipedia.org/w/index.php?title=Exponential_growth.
———. 2020c. “Incubation Period — Wikipedia, the Free Encyclopedia.” https://en.wikipedia.org/w/index.php?title=Incubation_period.